Large-scale Road Networks Research Articles

High-dimensional offline origin-destination (OD) demand calibration for stochastic traffic simulators of large-scale road networks

This paper considers high-dimensional offline calibration problems for large-scale simulation-based network models. We propose a metamodel simulation-based optimization (SO) approach. The proposed method is formulated and validated on a simple synthetic toy network. It is then applied to a high-dimensional case study of a large-scale Singapore network. Compared to two benchmark methods, a derivative-free pattern search method and the SPSA method, the proposed method improves the objective function estimates by two orders of magnitude. Moreover, this improvement is achieved after only 2 simulation runs. Hence, the proposed method is computationally efficient.The main idea of the proposed approach is to embed, within the SO algorithm, information from an analytical (i.e., lower-resolution) yet differentiable and tractable network model. It is this analytical structural information that enables the SO algorithm to become both suitable for high-dimensional problems and computationally efficient. For a network with n links, the analytical network model is implemented as a system of n nonlinear equations. Hence, it scales linearly with the number of links in the network and independently of link attributes (such as link length) and of the dimension of the route choice set.

Distributed Model Predictive Approach for Large-Scale Road Network Perimeter Control

In perimeter control, model predictive approaches have performed well in previous studies, but they were done in a centralized way. Considering real-time feasibility, extensibility, and functional stability, a distributed approach could be a promising solution particularly for cases with a large number of networks. In this paper, a new distributed model predictive approach is provided for large-scale road network perimeter control. In the model there are multiple local control agents which make their own decisions simultaneously at network boundaries after sharing necessary information. A control objective is provided upon the considerations of interaction between networks. Conditional weights are given for switching the control objective between greedy modes and cooperative modes. Case study results demonstrate that the proposed approach outperforms the fixed control and greedy control strategies.

Revealing heterogeneous spatiotemporal traffic flow patterns of urban road network via tensor decomposition-based clustering approach

Understanding the complex heterogeneity of traffic flow for the road network is of great importance to relieving traffic congestion and designing traffic control strategies. This study proposes a 3-stage pattern extraction approach to discover the heterogeneous spatiotemporal traffic flow patterns from the enormous observations: The non-negative tensor decomposition technique is utilized to extract the features of traffic flow in the within-one-day time, day-to-day and spatial segment modes; The spectral clustering is performed in the low-dimensional feature space, and the Gaussian similarity matrix is modified by the spatial proximity coefficient to integrate the spatial compactness into the clustering algorithm; The large-scale road network is partitioned into the independent and compact subnetworks with similar traffic congestion levels, and the macroscopic traffic flow relationships are fitted for each pattern. Research results indicate that the statistical distribution of traffic flow for each pattern is diverse; the heterogeneity of traffic patterns is verified by the low-scatter diagrams of macroscopic traffic flow relation for each subnetwork; the congested subnetworks are detected for both the peak hours and non-peak hours; the structures of subnetworks are highly concordant with the urban functional regions, varying with the different periods.

Wavelet-HST: A Wavelet-Based Higher-Order Spatio-Temporal Framework for Urban Traffic Speed Prediction

As a crucial part of the Intelligent Transportation System, traffic forecasting is of great help for traffic management and guidance. However, predicting short-term traffic conditions on a large-scale road network is challenging due to the complex spatio-temporal dependencies found in traffic data. Previous studies used Euclidean proximity or topological adjacency to explore the spatial correlation of traffic flows, but did not consider the higher-order connectivity patterns exhibited in a road network, which have a significant influence on traffic propagation. Meanwhile, traffic sequences display distinct multiple time-frequency properties, yet few researchers have made full use of this resource. To fill this gap, we propose a novel hybrid framework – Wavelet-based Higher-order Spatial-Temporal method (Wavelet-HST) to accurately predict network-scale traffic speeds. Wavelet-HST first uses discrete wavelet transform (DWT) to decompose raw traffic data into several components with different frequency sub-bands. Then a motif-based graph convolutional recurrent neural network (Motif-GCRNN) is proposed to learn the higher-order spatio-temporal dependencies of traffic speeds from low-frequency components, and auto-regressive moving average (ARMA) models are employed to simulate random fluctuations from the high-frequency components. We evaluate the framework on a traffic dataset collected in Chengdu, China, and experimental results demonstrate that Wavelet-HST outperforms six state-of-art prediction methods by an improvement of 7.8% ~10.5% in the root mean square error.

Parallel computing of KNN Query in road network based on MapReduce

K-nearest neighbor (KNN) query algorithm based on road network plays an important role in location based service, which had been widely used in intelligent transportation, roadside assistance and other fields. However, as road network density increases and the number of points of interest increases, query efficiency decreases sharply.In order to improve the query efficiency, we adopted the MapReduce parallel computing framework to complete the query of K neighbor moving objects by designing Map, Reduce, Combiner and other functions. Before the start of the query, the road network was divided into pieces, and each fragment was calculated. The final K-nearest neighbor moving objects were obtained by aggregating the calculated results of each slice to realize the parallel optimization of KNN algorithm based on road network. The experimental results showed that the performance of parallel KNN algorithm based on MapReduce was better than that of serial KNN query algorithm in large-scale road network environment and the larger K value of query request.

Improving the Performance of the Bilevel Solution for the Continuous Network Design Problem

For a long time, many researchers have investigated the continuous network design problem (CNDP) to distribute equitably additional capacity between selected links in a road network, to overcome traffic congestion in urban roads. In addition, CNDP plays a critical role for local authorities in tackling traffic congestion with a limited budget. Due to the mutual interaction between road users and local authorities, CNDP is usually solved using the bilevel modeling technique. The upper level seeks to find the optimal capacity enhancements of selected links, while the lower level is used to solve the traffic assignment problem. In this study, we introduced the enhanced differential evolution algorithm based on multiple improvement strategies (EDEMIS) for solving CNDP. We applied EDEMIS first to a hypothetical network to show its ability in finding the global optimum solution, at least in a small network. Then, we used a 16-link network to reveal the capability of EDEMIS especially in the case of high demand. Finally, we used the Sioux Falls city network to evaluate the performance of EDEMIS according to other solution methods on a medium-sized road network. The results showed that EDEMIS produces better solutions than other considered algorithms, encouraging transportation planners to use it in large-scale road networks.

Identifying spatiotemporal traffic patterns in large-scale urban road networks using a modified nonnegative matrix factorization algorithm

The identification and analysis of spatiotemporal traffic patterns in road networks constitute a crucial process for sophisticated traffic management and control. Traditional methods based on mathematical equations and statistical models can hardly be applicable to large-scale urban road networks, where traffic states exhibit high degrees of dynamics and complexity. Recently, advances in data collection and processing have provided new opportunities to effectively understand spatiotemporal traffic patterns in large-scale road networks using data-driven methods. However, limited efforts have been exerted to explore the essential structure of the networks when conducting a spatiotemporal analysis of traffic characteristics. To this end, this study proposes a modified nonnegative matrix factorization algorithm that processes high-dimensional traffic data and provides an improved representation of the global traffic state. After matrix factorization, cluster analysis is conducted based on the obtained low-dimensional representative matrices, which contain different traffic patterns and serve as the basis for exploring the temporal dynamics and spatial structure of network congestion. The applicability and effectiveness of the proposed approach are examined in a road network of Beijing, China. Results show that the methods exhibit considerable potential for identifying and interpreting the spatiotemporal traffic patterns over the entire network and provide a systematic and efficient approach for analyzing the network-level traffic state.

A Parallel-Computing Approach for Vector Road-Network Matching Using GPU Architecture

The road-network matching method is an effective tool for map integration, fusion, and update. Due to the complexity of road networks in the real world, matching methods often contain a series of complicated processes to identify homonymous roads and deal with their intricate relationship. However, traditional road-network matching algorithms, which are mainly central processing unit (CPU)-based approaches, may have performance bottleneck problems when facing big data. We developed a particle-swarm optimization (PSO)-based parallel road-network matching method on graphics-processing unit (GPU). Based on the characteristics of the two main stages (similarity computation and matching-relationship identification), data-partition and task-partition strategies were utilized, respectively, to fully use GPU threads. Experiments were conducted on datasets with 14 different scales. Results indicate that the parallel PSO-based matching algorithm (PSOM) could correctly identify most matching relationships with an average accuracy of 84.44%, which was at the same level as the accuracy of a benchmark—the probability-relaxation-matching (PRM) method. The PSOM approach significantly reduced the road-network matching time in dealing with large amounts of data in comparison with the PRM method. This paper provides a common parallel algorithm framework for road-network matching algorithms and contributes to integration and update of large-scale road-networks.

Parallel Hyper-Heuristic Algorithm for Multi-Objective Route Planning in a Smart City

Most of the commercial navigation products provide route planning service for users. However, they only consider a single metric such as distance, time, or other costs, while ignoring a critical criterion: safety. In a smart city, people may prefer to find a safe walking route to avoid the potential crime risk as well as obtain a short distance. This problem can be specified as a multi-objective optimization problem (MOOP). Many methods were proposed in the past to solve the multi-objective route planning, the multi-objective evolutionary approach (MOEA) is considered as the most popular one. However, MOEA is non-optimized when used in a large-scale road network and becomes computationally expensive when handling a large population size. In this paper, we propose a multi-objective hyper-heuristic (MOHH) framework for walking route planning in a smart city. In the search framework, we design a set of low level heuristics to generate new routes. Moreover, we adopt reinforcement learning mechanism to select good low-level heuristics to accelerate searching speed. We further improve the reinforcement learning-based multi-objective hyper-heuristic (RL-MOHH) algorithm and implement a parallel version (RL-PMOHH) on general purpose graphic process unit. Extensive experiments are conducted on the safety-index map constructed from the historical urban data of the New York city. Comprehensive experimental results show that the proposed RL-PMOHH is almost 173, 5.3, and 3.1 times faster than the exact multi-objective optimization algorithm, the RL-MOHH algorithm, and the parallel NSGA-II algorithm, respectively. Moreover, both RL-MOHH and RL-PMOHH can obtain more than 80% Pareto optimal solutions in a large-scale road network.

Efficient continuous top-k geo-image search on road network

With the rapid development of mobile Internet and cloud computing technology, large-scale multimedia data, e.g., texts, images, audio and videos have been generated, collected, stored and shared. In this paper, we propose a novel query problem named continuous top-$k$ geo-image query on road network which aims to search out a set of geo-visual objects based on road network distance proximity and visual content similarity. Existing approaches for spatial textual query and geo-image query cannot address this problem effectively because they do not consider both of visual content similarity and road network distance proximity on road network. In order to address this challenge effectively and efficiently, firstly we propose the definition of geo-visual objects and continuous top-$k$ geo-visual objects query on road network, then develop a score function for search. To improve the query efficiency in a large-scale road network, we propose the search algorithm named geo-visual search on road network based on a novel hybrid indexing framework called VIG-Tree, which combines G-Tree and visual inverted index technique. In addition, an important notion named safe interval and results updating rule are proposed, and based on them we develop an efficient algorithm named moving monitor algorithm to solve continuous query. Experimental evaluation on real multimedia dataset and road network dataset illustrates that our solution outperforms state-of-the-art method.

The MapReduce-based approach to improve the shortest path computation in large-scale road networks: the case of A* algorithm

This paper deals with an efficient parallel and distributed framework for intensive computation with A* algorithm based on MapReduce concept. The A* algorithm is one of the most popular graph traversal algorithm used in route guidance. It requires exponential time computation and very costly hardware to compute the shortest path on large-scale networks. Thus, it is necessary to reduce the time complexity while exploiting a low cost commodity hardwares. To cope with this situation, we propose a novel approach that reduces the A* algorithm into a set of Map and Reduce tasks for running the path computation on Hadoop MapReduce framework. An application on real road networks illustrates the feasibility and reliability of the proposed framework. The experiments performed on a 6-node Hadoop cluster proves that the proposed approach outperforms A* algorithm and achieves significant gain in terms of computation time.

Distributed adaptive perimeter control for large-scale urban road networks with delayed state interconnections

Distributed adaptive perimeter control is a promising paradigm for controlling large-scale road networks. Recently, coordinated distributed perimeter control of interconnected urban road regions has been developed in Haddad and Mirkin (2015). A Macroscopic Fundamental Diagram (MFD)-based model was utilized to describe the aggregate traffic flow dynamics, and model reference adaptive control was implemented to control the distributed perimeter controllers. Previous works, including Haddad and Mirkin (2015), implicitly postulated that during the control process, all needed information for computing are obtained in real time and processed instantly. However, collecting, processing, and uploading traffic data in a large-scale urban road networks impose time delays. Towards developing realistic aggregate models and improving perimeter control algorithms, the current paper aims at addressing the existence of such time delays. Hence, delayed state interconnections are incorporated in the dynamic equations to model data processing and communication delays between surrounding interconnected regions. Based on developed model, distributed perimeter control scheme in presence of time delays in interconnections is implemented.

Cognition-based hierarchical en route planning for multi-agent traffic simulation

Abstract The goal of this study is to model drivers’ cognition-based en route planning behaviors in a large-scale road network via the Extended Belief-Desire-Intention (E-BDI) framework. E-BDI is a probabilistic behavior modeling framework based on agents’ own preferences of multiple attributes (e.g., travel time and its variance) and daily driving experiences. However, it is challenging to use the E-BDI framework for the demonstration of drivers’ en route planning behavior in a large-scale road network due to its high computational demand. To handle the computation issue, a hierarchical en route planning approach is proposed in this study. The proposed E-BDI-based en route planning approach consists of three major procedures: (1) network partitioning, (2) network aggregation, and (3) E-BDI-based en route planning. The Java-based E-BDI module integrated with DynusT® traffic simulation software is developed to demonstrate the proposed en route planning approach in Phoenix, Arizona road network involving 11,546 nodes and 24,866 links. The demonstration results reveal that the proposed approach is computationally efficient and effective in representing various en route planning behaviors of drivers in a large-scale road network.

Modeling and Density Estimation of an Urban Freeway Network Based on Dynamic Graph Hybrid Automata.

In this paper, in order to describe complex network systems, we firstly propose a general modeling framework by combining a dynamic graph with hybrid automata and thus name it Dynamic Graph Hybrid Automata (DGHA). Then we apply this framework to model traffic flow over an urban freeway network by embedding the Cell Transmission Model (CTM) into the DGHA. With a modeling procedure, we adopt a dual digraph of road network structure to describe the road topology, use linear hybrid automata to describe multi-modes of dynamic densities in road segments and transform the nonlinear expressions of the transmitted traffic flow between two road segments into piecewise linear functions in terms of multi-mode switchings. This modeling procedure is modularized and rule-based, and thus is easily-extensible with the help of a combination algorithm for the dynamics of traffic flow. It can describe the dynamics of traffic flow over an urban freeway network with arbitrary topology structures and sizes. Next we analyze mode types and number in the model of the whole freeway network, and deduce a Piecewise Affine Linear System (PWALS) model. Furthermore, based on the PWALS model, a multi-mode switched state observer is designed to estimate the traffic densities of the freeway network, where a set of observer gain matrices are computed by using the Lyapunov function approach. As an example, we utilize the PWALS model and the corresponding switched state observer to traffic flow over Beijing third ring road. In order to clearly interpret the principle of the proposed method and avoid computational complexity, we adopt a simplified version of Beijing third ring road. Practical application for a large-scale road network will be implemented by decentralized modeling approach and distributed observer designing in the future research.

Citywide Estimation of Traffic Dynamics via Sparse GPS Traces

Traffic congestion is a perpetual challenge in metropolitan areas around the world. The ability to understand traffic dynamics is thus critical to effective traffic control and management. However, estimation of traffic conditions over a large-scale road network has proven to be a challenging task for two reasons: first, traffic conditions are intrinsically stochastic; second, the availability and quality of traffic data vary to a great extent. Traditional traffic monitoring systems that exist mostly on major roads and highways are insufficient to recover the traffic conditions for an entire network. Recent advances in GPS technology and the resulting rich data sets offer new opportunities to improve upon such traditional means, by providing much broader coverage of road networks. Despite that, such data are limited by their spatial-temporal sparsity in practice. To address these issues, we have developed a novel framework to estimate travel times, traversed paths, and missing values over a large-scale road network using sparse GPS traces. Our method consists of two phases. In the first phase, we adopt the shortest travel time criterion based on Wardrop's Principles in the map-matching process. With an improved traveltime allocation technique, we have achieved up to 52.5% relative error reduction in network travel times compared to a state-of-the-art method [1]. In the second phase, we estimate missing values using Compressed Sensing algorithm, thereby reducing the number of required measurements by 94.64%.

Time Coefficient Estimation for Hourly Origin-Destination Demand from Observed Link Flow Based on Semidynamic Traffic Assignment

Day-long origin-destination (OD) demand estimation for transportation forecasting is advantageous in terms of accuracy and reliability because it is not affected by hourly variations in the OD distribution. In this paper, we propose a method to estimate the time coefficient of day-long OD demand to estimate hourly OD demand and to predict hourly traffic for urban transportation planning of a large-scale road network that lacks discrete-time rich traffic data. The model proposed estimates the time coefficients from observed link flows given a proven day-long OD demand based on a bilevel formulation of the generalized least square and semidynamic traffic assignment (OD-modification approach). The OD-modification approach is formulated as a static user-equilibrium assignment with elastic demand, based on the residual demand at the end of each period. Our model does not require setting many parameters regarding the OD demand matrices and the discrete-time dynamic traffic assignments. Applying the model to large-scale road network demonstrates that it efficiently improves estimation accuracy because the 24-hour time coefficients of survey data are slightly biased and may be modified properly. In addition, the methods that partially relax the assumption of OD-modification approach and transform the estimated demand into demand based on departure time are examined.

НЕЙРОСЕТЕВЫЕ ПОДХОДЫ К УПРАВЛЕНИЮ ПОТОКАМИ ТРАНСПОРТА

A problem of optimal urban traffic flows control is considered. A mathematical model of control by the traffic lights at intersections using the controlled networks theory is given. It is a system of nonlinear finite-differential equations. To present a large scale road networks the model contains the connection matrices that describe interactions between input and output roads in subnetworks. The traffic flow control is performed by the coordination of active phases of traffic lights. A control goal is to minimize the difference between the total input flow and total output flow for all subnetworks. In this paper, a neural network approach for urban traffic road network parameters adjustment is presented. A simulation is conducted under a microscopic traffic simulation software CTraf. Results demonstrate that neural network reinforcement training obtain good parameters of the network model.

Intelligent Traffic Flows Control Software for Megapolis

A problem of traffic flows control in congestion conditions is considered. Control is performed on intersections using optimal traffic lights phases durations. The traffic lights phases durations are searched using previously proposed traffic flows mathematical model which is based on the controlled networks theory and evolutionary algorithms. To present a large scale road networks the model contains the connection matrices that describe interactions between input and output roads in subnetworks. Results obtained on traffic simulation software CTraf are given.

Predicting travel time reliability using mobile phone GPS data

Estimates of road speeds have become commonplace and central to route planning, but few systems in production provide information about the reliability of the prediction. Probabilistic forecasts of travel time capture reliability and can be used for risk-averse routing, for reporting travel time reliability to a user, or as a component of fleet vehicle decision-support systems. Many of these uses (such as those for mapping services like Bing or Google Maps) require predictions for routes in the road network, at arbitrary times; the highest-volume source of data for this purpose is GPS data from mobile phones. We introduce a method (TRIP) to predict the probability distribution of travel time on an arbitrary route in a road network at an arbitrary time, using GPS data from mobile phones or other probe vehicles. TRIP captures weekly cycles in congestion levels, gives informed predictions for parts of the road network with little data, and is computationally efficient, even for very large road networks and datasets. We apply TRIP to predict travel time on the road network of the Seattle metropolitan region, based on large volumes of GPS data from Windows phones. TRIP provides improved interval predictions (forecast ranges for travel time) relative to Microsoft’s engine for travel time prediction as used in Bing Maps. It also provides deterministic predictions that are as accurate as Bing Maps predictions, despite using fewer explanatory variables, and differing from the observed travel times by only 10.1% on average over 35,190 test trips. To our knowledge TRIP is the first method to provide accurate predictions of travel time reliability for complete, large-scale road networks.

Efficient Geo-Computational Algorithms for Constructing Space-Time Prisms in Road Networks

The Space-time prism (STP) is a key concept in time geography for analyzing human activity-travel behavior under various Space-time constraints. Most existing time-geographic studies use a straightforward algorithm to construct STPs in road networks by using two one-to-all shortest path searches. However, this straightforward algorithm can introduce considerable computational overhead, given the fact that accessible links in a STP are generally a small portion of the whole network. To address this issue, an efficient geo-computational algorithm, called NTP-A*, is proposed. The proposed NTP-A* algorithm employs the A* and branch-and-bound techniques to discard inaccessible links during two shortest path searches, and thereby improves the STP construction performance. Comprehensive computational experiments are carried out to demonstrate the computational advantage of the proposed algorithm. Several implementation techniques, including the label-correcting technique and the hybrid link-node labeling technique, are discussed and analyzed. Experimental results show that the proposed NTP-A* algorithm can significantly improve STP construction performance in large-scale road networks by a factor of 100, compared with existing algorithms.